000			01941nam a2200289 i 4500
003			IN-BdCUP
005			20250103125021.0
008			180510s2020||||enk o ||1 0|eng|d
020			_a9781108608480 (ebook) _z9781108497329 (hardback)
040			_aIN-BdCUP _beng _cIN-BdCUP _erda
041			_aeng
050			_aP308 _b.K638 2020
082			_a418/.020285
100			_aKoehn, Philipp _eAuthor
245		0	_aNeural machine translation / _cPhilipp Koehn.
264			_aCambridge : _bCambridge University Press, _c2020
300			_a1 online resource (xiv, 393 pages) : _bdigital, PDF file(s).
336			_atext _btxt
337			_2rdamedia
338			_aonline resource _bcr _2rdacarrier
500			_aTitle from publisher's bibliographic system (viewed on 01 Jun 2020).
520			_aDeep learning is revolutionizing how machine translation systems are built today. This book introduces the challenge of machine translation and evaluation - including historical, linguistic, and applied context -- then develops the core deep learning methods used for natural language applications. Code examples in Python give readers a hands-on blueprint for understanding and implementing their own machine translation systems. The book also provides extensive coverage of machine learning tricks, issues involved in handling various forms of data, model enhancements, and current challenges and methods for analysis and visualization. Summaries of the current research in the field make this a state-of-the-art textbook for undergraduate and graduate classes, as well as an essential reference for researchers and developers interested in other applications of neural methods in the broader field of human language processing.
650			_aMachine translation. _aNeural networks (Computer science)
776			_iPrint version: _z9781108608480
856			_3Electronic Book Resource _uhttps://doi.org/10.1017/9781108608480
942			_2ddc _cE
999			_c54658 _d54658